• Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity

The project involves writing clean code for churn prediction. At its core is churn_library.py, an end-to-end script for predicting customer churn with data import, EDA, feature engineering and model training functions.

To test the main functions in churn_library run: churn_script_logging_and_tests.py

churn_library.py

churn_notebook.py

churn_script_logging_and_tests.py

Guide.ipynb

README.md

requirements_py3.6.txt

requirements_py3.8.txt

data

• bank_data.csv

images

• eda
  • churn_hist.png
  • customer_age_hist.png
  • heatmap.png
  • marital_status_bar.png
  • total_transactions_hist.png
• results
  • feat_importances.png
  • linear_reg_clf_results.png
  • random_forest_clf_results.png
  • roc_curve.png

logs

• churn_library.log

models

• logistic_model.pkl
• rfc_model.pkl

The project was built from within an Anaconda environment. To run it in an isolated virtual environment, make sure you have Continuum's Anaconda installed and follow these steps.

1. Create a new virtual environment with your preferred Python version: conda create --name <env_name> python=3.8

2. Activate the new virtual environment: conda activate <env_name> (to deactivate: conda deactivate)

3. Install pip in the new virtual environment: conda install pip

4. Use pip to install dependencies: pip install -r requirements_py3.8.txt

5. Open the project files in Jupyter Notebook. For best results, install ipykernel so that the new virtual environment with the required packages can be accessed directly from the notebook: conda install ipykernel